PREDICTIVE ANALYSIS IN MEDICAL HEALTHCARE: A META-ANALYSIS

Sheza Waqar Beg; Sharique Ahmad; Subuhi Anwar; Tanish Baqar; Priyesh Srivastava; Priyanka Sharma

doi:10.29121/granthaalayah.v12.i6.2024.5668

Authors

Sheza Waqar Beg Senior Associate, UpGrad, Smartworks - Fleet House, Marol, Andheri East, Mumbai, India
Sharique Ahmad Professor, Dept. Of Pathology & Blood Bank, Era's Lucknow Medical College & Hospital, Era University, Lucknow, Uttar Pradesh, India-22600
Subuhi Anwar Research Assistant, Department of Pathology, Era’s Lucknow Medical College and Hospital, Sarfarzganj, Hardoi Road, Lucknow, India
Tanish Baqar Intern, Era’s Lucknow Medical College Hospital, Era University Lucknow, India
Priyesh Srivastava JR 1, Department of Pathology Era’s Lucknow Medical College Hospital, Era University Lucknow, India
Priyanka Sharma JR 2, Department of Pathology Era’s Lucknow Medical College Hospital, Era University Lucknow, India

DOI:

https://doi.org/10.29121/granthaalayah.v12.i6.2024.5668

Keywords:

Predictive Analytics, Data Mining, Statistical Modelling, Machine Learning, Healthcare

Abstract [English]

Predictive analytics is a subfield of advanced analytics that uses historical data along with statistical modelling, data mining, and machine learning to forecast future events. Businesses use predictive analytics to look for trends in this data to pinpoint possibilities and dangers. This meta-analysis explores the landscape of predictive analysis within medical healthcare, examining methodologies, applications, challenges, and future directions. By synthesizing existing literature, this study offers insights into the effectiveness, limitations, and potential advancements in predictive analytics within the healthcare domain. This meta-analysis aims to provide a comprehensive overview of the state of predictive analysis in medical healthcare, highlighting key methodologies, applications, challenges, and future directions. To conduct this meta-analysis, a systematic approach was employed. Inclusion criteria encompassed studies focusing on predictive analysis in medical healthcare published in peer-reviewed journals. Databases such as PubMed, IEEE Xplore, and Scopus were searched using relevant keywords. Data extraction involved identifying key methodologies, applications, and challenges discussed in each study. Quality assessment was performed to ensure the reliability of included studies and minimize bias.

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